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cbir-content-based-image-retrieval- icon cbir-content-based-image-retrieval-

本次实训按照指导书要求,设计并实现了一个简单的图像检索系统,实现了从颜色特征、纹理特征、形状特征三个方面对图像进行匹配。其中颜色特征是提取自RGB颜色空间的颜色矩;纹理特征是来自图像0度、45度、90度、135度四个角度的灰度共生矩阵的纹理一致性、纹理对比度、纹理熵,纹理相关性的期望和标准差;形状特征是采用形状不变矩法提取了图像Hu不变矩u1~u7和离心率e。在图像匹配方面采用了欧氏距离和余弦定理计算相似度。用户在使用该系统时可以设置单特征或某权重比例下的多特征检索,系统将会自动对源图像进行处理并返回库图中相似度最高的图片。

cbir-system icon cbir-system

一个本地的基于内容的图像检索系统,实现了包括颜色特征提取(颜色直方图,HSV中心距),纹理特征(灰度共生矩阵,LBP算子),边缘特征(边缘直方图),哈希感知算法(aHash,pHash,dHash算法等),SIFT特征提取。以及基于VGG-16提取特征等功能

waiter-facerecognition-python icon waiter-facerecognition-python

(后端Python)本科毕业设计-基于深度学习的人脸识别考勤系统,具备基础的人脸录入,人脸识别,考勤管理,课堂管理,班级管理,日志管理等功能。此Python项目是整体项目的人脸识别部分,基于深度学习的FaceNet算法,对人脸特征进行提取,判断是否输入同一张人脸,提供人脸认证接口,对数据库进行操作。

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